Scala vs Python for Apache Spark: An In-depth Comparison

What is Scala?

Scala, an acronym for “scalable language,” is a general-purpose, concise, high-level programming language that combines functional programming and object-oriented programming. It runs on JVM (Java Virtual Machine) and interoperates with existing Java code and libraries.

Many programmers find Scala code to be error-free, concise, and readable, making it simple to use for writing, compiling, debugging, and running programs, particularly compared to other languages. Scala’s developers elaborate on these concepts, adding “Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.”

What is Python?

Python developers define the language as “…an interpreted, object-oriented, a high-level programming language with dynamic semantics. Its high-level built-in data structures, combined with dynamic binding and dynamic typing, which makes it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together.”

Programmers like Python because of its relative simplicity, support of multiple packages and modules, and its interpreter and standard libraries are available for free. These advantages, and many others besides, compel programmers to learn Python.

What is Apache Spark?

Apache Spark is an open-source, unified analytics engine used for processing Big Data. It is considered the primary platform for batch processing, large-scale SQL, machine learning, and stream processing—all done through intuitive, built-in modules.

Spark is a general-purpose, cluster computing framework that rapidly performs processing tasks with extensive datasets. The framework can also distribute data processing tasks across many nodes, by itself or simultaneously with other distributed computing tools.

Hadoop is Apache Spark’s most well-known rival, but the latter is evolving faster and is posing a severe threat to the former’s prominence. Many organizations favor Spark’s speed and simplicity, which supports many available application programming interfaces (APIs) from languages like Java, R, Python, and Scala.

Here’s a more detailed and informative look at the Spark vs. Hadoop frameworks. If the article convinces you to learn more about Spark, then consider looking at this Spark tutorial.

What is Scala Used For?

Anything you use Java for, you can use Scala instead. It’s ideal for back-end code, scripts, software development, and web design. Programmers also tout Scala’s seamless integration of object-oriented features and functional languages as the perfect tool for parallel batch processing, data analysis using Spark, AWS Lambda expressions, and ad hoc scripting with REPL.

Companies currently using Scala include:

• 9GAG
• Asana
• Groupon
• LinkedIn
• Reddit
• Twitter

What is Python Used For?

Python’s simplicity and simple to learn syntax make it the ideal choice for developing a desktop graphical user interface (GUI) applications, web applications, and websites. Its emphasis on readability makes it a cost-effective option, particularly in terms of maintenance.

Furthermore, Python’s ecosystem is an ideal resource for machine learning and artificial intelligence (AI), two of today’s increasingly deployed technologies. Python’s syntax resembles the English language, creating a more comfortable and familiar environment for learning.

Companies and organizations currently leveraging Python include:

• Dropbox
• Instagram
• NASA
• Netflix
• Spotify
• Uber Technologies

Why Learn Scala For Spark?

Now that we have been introduced to the primary players, let’s discuss why Scala for Spark is a smart idea. We’ve seen earlier that Spark has a Scala API (one of many). So why would Scala stand out?

Here are five compelling reasons why you should learn Scala programming.

• Spark is written in Scala

  When you want to get the most out of a framework, you need to master its original language. Scala is not only Spark’s programming language, but it’s also scalable on JVM. Scala makes it easy for developers to go deeper into Spark’s source code to get access and implement all the framework’s newest features.

• Scala is Less Cumbersome and Cluttered than Java

  One complex line of Scala code replaces between 20 to 25 lines of Java code. Scala’s simplicity is a must for Big Data processors. As a bonus, there’s robust interoperability between Scala and Java code— Scala developers can also use their Scala code to access Java libraries directly.

• Balance
  

  Scala strikes a reasonable balance between performance and productivity. Even beginner level Spark developers can be brought up to speed quickly thanks to Scala’s simplicity. But Scala’s lack of complexity doesn’t diminish the potential for the productivity it enables.

• Scala is Very Popular
  

  As mentioned earlier, many influential businesses and organizations use or have migrated to Scala. Additionally, Scala has a brighter future in many ways. For instance, and as more people become aware of its ease of scalability, even big financial institutions and investment banks are gradually turning to Scala to provide the low-latency solutions they require.

• Parallelism and Concurrency
  

  Scala’s design creates an environment well suited for both these computations types. Frameworks such as Akka, Lift and Play help programmers design better applications on JVM.

Overall, Which Language is Better?

The best way to answer the “Scala vs. Python” question is by first comparing each language, broken down by features.

• Definition
  

  Scala is categorized as an object-oriented, statically typed programming language, so programmers must specify object types and variables. Python is a dynamically typed object-oriented programming languages, requiring no specification.

• Performance
  

  Scala clocks in at ten times faster than Python, thanks to the former’s static type language.

• Ease of Use
  

  Scala is easier to learn than Python, though the latter is comparatively easy to understand and work with and is considered overall more user-friendly.

• Concurrency
  

  Scala handles concurrency and parallelism very well, while Python doesn’t support true multi-threading.

• Learning Curve
  

  Scala is more complex, compared to Python. The latter’s syntax and standard libraries contribute much to the language’s simplicity.

• Type-Safety
  

  Static-typed variables can’t change. Scala is a static-typed language, and Python is a dynamically typed language. Type-safety makes Scala a better choice for high-volume projects because its static nature lends itself to faster bug and compile-time error detection.

• Community Support
  

  Compared to Scala, Python has a vast community from which it can draw support. Consequently, Python enjoys more extensive libraries dedicated to different task complexities. Note, however, that Scala does enjoy strong support; however, it pales in comparison with Python.

• Project Scale
  

  Python works better for small projects, while Scala is best suited for large-scale projects.

So, which programming language is better? Boring answer, but it depends on what your project needs are. If you want to work on a smaller project with less experienced programmers, then Python is the smart choice. However, if you have a massive project that needs many resources and parallel processing, then Scala is the best way to go.

Increasing Your Big Data Knowledge and Skills

There is a lot to learn about Big Data, including its tools, associated programming languages, and other related resources. Simplilearn has a useful collection of topical information—from tips on how to boost your skills to ways to help you prepare for an upcoming job interview.

For example, this Spark Scala tutorial helps you establish a solid foundation on which to build your Big Data-related skills. Follow this up by practicing for Spark and Scala exams with these Spark exam dumps.

Before embarking on that crucial Spark or Python-related interview, you can give yourself an extra edge with a little preparation. Check out these Spark interview questions or Python interview questions, and reduce the chance of getting caught without a knowledgeable answer.

Professionals already involved in Big Data can benefit from these resources, too. The best professionals, regardless of their field, engage in continuing education and upskilling. After all, the industry changes fast, and there’s always something new to learn! Upskilling increases your knowledge base and makes you a more valuable and viable candidate should you decide to look for a new position elsewhere.

Do You Want a Career in Big Data Analytics?

Independent research and exam preparation questions are exceptional tools for strengthening your command of Big Data concepts. Still, you need Simplilearn’s Apache Spark and Scala Certification Training course to complete your skillset.

The course helps you advance your mastery of the Big Data Hadoop Ecosystem, teaching you the crucial, in-demand Apache Spark skills, and helping you develop a competitive advantage for a rewarding career as a Hadoop developer.

You will master the essential skills of the Apache Spark open-source framework and Scala programming language. Learn Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. You will even learn how to overcome MapReduce’s limitations by using Spark.

There is an ever-growing demand for Big Data analytics professionals. According to Ziprecruiter, a Big Data analyst’s average annual salary nationwide comes in at USD 130,223. A Big Data analyst career offers security, an exciting challenge, and excellent financial compensation. Take those first steps and visit Simplilearn today!